SmartDataWithR

SmartDataWithR

#%% packages
from shiny import Inputs, Outputs, Session, App, reactive, render, req, ui
import pandas as pd
import numpy as np
from plotnine import ggplot, aes, geom_line, theme, element_text, labs
#%% data prep
# data source: https://www.kaggle.com/datasets/muhammadkhalid/most-popular-programming-languages-since-2004

# languages = pd.read_csv('MostPopularProgrammingLanguages.csv')
languages = pd.DataFrame({"Date": {"0": "July 2004", "1": "August 2004", "2": "September 2004", "3": "October 2004", "4": "November 2004", "5": "December 2004", "6": "January 2005", "7": "February 2005", "8": "March 2005", "9": "April 2005", "10": "May 2005", "11": "June 2005", "12": "July 2005", "13": "August 2005", "14": "September 2005", "15": "October 2005", "16": "November 2005", "17": "December 2005", "18": "January 2006", "19": "February 2006", "20": "March 2006", "21": "April 2006", "22": "May 2006", "23": "June 2006", "24": "July 2006", "25": "August 2006", "26": "September 2006", "27": "October 2006", "28": "November 200

SmartDataWithR

#%% packages
from shiny import App, render, ui, reactive
from plotnine import ggplot, aes, geom_tile, labs
import pandas as pd
import numpy as np

#%% init the dataframe
cnt_doors = 101
df = pd.DataFrame({'iteration': np.arange(1, cnt_doors), 'door_is_open': [False] * (cnt_doors-1)})
#%%

SmartDataWithR

#%% packages
from shiny import Inputs, Outputs, Session, App, reactive, render, req, ui
import pandas as pd
import numpy as np
from plotnine import ggplot, aes, geom_line, theme, element_text, labs

SmartDataWithR

languages = pd.DataFrame({"Date": {"0": "July 2004", "1": "August 2004", "2": "September 2004", "3": "October 2004", "4": "November 2004", "5": "December 2004", "6": "January 2005", "7": "February 2005", "8": "March 2005", "9": "April 2005", "10": "May 2005", "11": "June 2005", "12": "July 2005", "13": "August 2005", "14": "September 2005", "15": "October 2005", "16": "November 2005", "17": "December 2005", "18": "January 2006", "19": "February 2006", "20": "March 2006", "21": "April 2006", "22": "May 2006", "23": "June 2006", "24": "July 2006", "25": "August 2006", "26": "September 2006", "27": "October 2006", "28": "November 2006", "29": "December 2006", "30": "January 2007", "31": "February 2007", "32": "March 2007", "33": "April 2007", "34": "May 2007", "35": "June 2007", "36": "July 2007", "37": "August 2007", "38": "September 2007", "39": "October 2007", "40": "November 2007", "41": "December 2007", "42": "January 2008", "43": "February 2008", "44": "March 2008", "45": "April 2008", "46": "May 2008", "4

SmartDataWithR

languages['datetime'] = pd.to_datetime(languages['Date'])  # 1
languages.drop(axis=1, columns=['Date'], inplace=True)  # 2

languages_long = languages.melt(id_vars='datetime', value_name='popularity', var_name='language')  # 3
date_range_start = np.min(languages_long['datetime'])  # 4
date_range_end = np.max(languages_long['datetime'])  # 5

language_names = languages_long['language'].unique()  # 6
langugage_names_dict = {l:l for l in language_names}  # 7

SmartDataWithR

app_ui = ui.page_fluid(
    ui.panel_title('Most Popular Programming Languages'),  # 1
    ui.layout_sidebar(
        ui.panel_sidebar(
            ui.input_selectize(id="language", label="Languages", choices=langugage_names_dict, selected='Python', multiple=True),  # 2
            ui.input_date_range(id='date_range', label='Date Range', start=date_range_start, end=date_range_end, ),  # 3
            
        ),
        ui.panel_main(
            ui.output_plot("plotTimeseries"),  # 4

SmartDataWithR

def server(input: Inputs, output: Outputs, session: Session):
    
    # 1
    @reactive.Calc
    def languages_filt():
        date_selected_start = str(input.date_range()[0])
        date_selected_end = str(input.date_range()[1])
        l = languages_long.loc[(languages_long['language'].isin(input.language())) & (languages_long['datetime']>= date_selected_start) & (languages_long['datetime']<= date_selected_end)]
        return l

SmartDataWithR

shiny
pandas
plotnine

SmartDataWithR

#%% packages
# data handling
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score
# deep learning
import torch
import torch.nn as nn